comprehensive implementation of the Ai powered genetic testing platform

Genetic.md

A comprehensive implementation of an Ai powered genetic testing platform, complete with detailed instructions and documentation. This guide will walk you through setting up each component, ensuring a fully functional system.

Table of Contents

1. System Overview
2. Project Structure
3. Environment Setup
4. Configuration Management
5. Database Integration
6. MinION Controller Implementation
7. Machine Learning Pipeline
8. Analysis Pipeline
9. API Development with FastAPI
10. Testing
11. Deployment
12. References

---

System Overview

The genetic testing platform is designed to perform rapid sequencing and analysis using Oxford Nanopore's MinION device. It integrates real-time data processing, modification detection, and machine learning (ML) analysis of genetic edits. The system architecture includes:

• Configuration Management: Using Pydantic for settings management.
• Database Integration: SQLAlchemy for interacting with a PostgreSQL database.
• MinION Control Interface: Controlling the MinION device using the MinKNOW API.
• Machine Learning Pipeline: PyTorch-based models for sequence analysis.
• RESTful API: FastAPI for exposing endpoints to interact with the system.
• Dependency Management: Poetry for managing project dependencies.

---

Project Structure

Organize your project as follows:

genetic_testing/
├── pyproject.toml
├── config/
│   └── settings.py
├── src/
│   ├── core/
│   │   ├── minion_controller.py
│   │   └── database.py
│   ├── models/
│   │   └── schemas.py
│   ├── pipeline/
│   │   ├── sequencing.py
│   │   └── analysis.py
│   ├── ml/
│   │   ├── autoencoder.py
│   │   └── classifier.py
│   └── main.py
└── tests/
    └── test_pipeline.py

• config/: Configuration files and settings.
• src/core/: Core functionalities like database and MinION controller.
• src/models/: Data models and schemas.
• src/pipeline/: Sequencing and analysis pipelines.
• src/ml/: Machine learning models.
• tests/: Unit and integration tests.

---

Environment Setup

1. Install Python and Poetry

Ensure you have Python 3.9 or higher installed. Install Poetry for dependency management:

pip install poetry

2. Initialize the Project

Navigate to your project directory and initialize it:

cd genetic_testing
poetry init

Follow the prompts to set up your pyproject.toml file.

3. Add Dependencies

Add the required dependencies:

poetry add fastapi sqlalchemy pydantic torch biopython minknow-api numpy uvicorn

4. Set Up a Virtual Environment

Create a virtual environment and activate it:

poetry shell

---

Configuration Management

config/settings.py

Create a settings file to manage configurations using Pydantic:

# config/settings.py
from pydantic import BaseSettings, Field
from typing import Optional

class Settings(BaseSettings):
    # Database settings
    DATABASE_URL: str = Field(..., env="DATABASE_URL")

    # MinION settings
    MINION_HOST: str = Field(default="localhost", env="MINION_HOST")
    MINION_PORT: int = Field(default=9502, env="MINION_PORT")
    FLOW_CELL_ID: str = Field(..., env="FLOW_CELL_ID")

    # ML Pipeline settings
    MODEL_PATH: str = Field(default="models/classifier.pt", env="MODEL_PATH")
    BATCH_SIZE: int = Field(default=32, env="BATCH_SIZE")
    MODIFICATION_THRESHOLD: float = Field(default=0.1, env="MODIFICATION_THRESHOLD")

    class Config:
        env_file = ".env"

# Instantiate settings
settings = Settings()

.env File

Create a .env file in your project root to store environment variables:

DATABASE_URL=postgresql://user:password@localhost:5432/genetic_db
FLOW_CELL_ID=YOUR_FLOW_CELL_ID

---

Database Integration

src/core/database.py

Set up SQLAlchemy to interact with the PostgreSQL database:

# src/core/database.py
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker, declarative_base
from config.settings import settings

engine = create_engine(settings.DATABASE_URL)
SessionLocal = sessionmaker(bind=engine)
Base = declarative_base()

def get_db():
    """Dependency that provides a database session."""
    db = SessionLocal()
    try:
        yield db
    finally:
        db.close()

src/models/schemas.py

Define your database models and Pydantic schemas:

# src/models/schemas.py
from sqlalchemy import Column, Integer, String, JSON
from src.core.database import Base

class SampleResult(Base):
    __tablename__ = 'sample_results'
    id = Column(Integer, primary_key=True, index=True)
    sample_id = Column(String, unique=True, index=True)
    sequence = Column(String)
    modifications = Column(JSON)
    edit_analysis = Column(JSON)

Create Database Tables

Use Alembic or SQLAlchemy's create_all method to create database tables:

# Create tables (for development purposes)
from src.core.database import engine, Base
Base.metadata.create_all(bind=engine)

---

MinION Controller Implementation

src/core/minion_controller.py

Implement the MinION controller using the MinKNOW API:

# src/core/minion_controller.py
import asyncio
from minknow_api.manager import Manager
from minknow_api.protocol import ProtocolService
from minknow_api import Connection
from config.settings import settings

class MinIONController:
    def __init__(self):
        self.connection = Connection(
            host=settings.MINION_HOST,
            port=settings.MINION_PORT
        )
        self.manager = Manager(self.connection)
        self.position = self.manager.device
        self.protocol = ProtocolService(self.connection)

    async def start_sequencing(self, sample_id: str):
        """Starts the sequencing protocol."""
        protocol_id = "sequencing/sequencing_MIN106_DNA"
        run_id = await self.protocol.start_protocol(
            protocol_id=protocol_id,
            sample_id=sample_id
        )
        return run_id

    async def get_sequence_data(self):
        """Retrieves raw sequencing data."""
        # Implement data retrieval logic
        pass

Notes

• Asynchronous Programming: The MinKNOW API uses asyncio; ensure your functions are async def and use await.
• Protocol Identification: Use the correct protocol ID as per your MinION device and flow cell.

---

Machine Learning Pipeline

src/ml/autoencoder.py

Implement the autoencoder for modification detection:

# src/ml/autoencoder.py
import torch
import torch.nn as nn

class SequenceAutoencoder(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(SequenceAutoencoder, self).__init__()
        self.encoder = nn.Sequential(
            nn.Linear(input_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, hidden_size // 2)
        )
        self.decoder = nn.Sequential(
            nn.Linear(hidden_size // 2, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, input_size)
        )

    def forward(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded

Model Training

Implement a script to train your autoencoder model on your dataset:

# src/ml/train_autoencoder.py
import torch
from torch.utils.data import DataLoader, TensorDataset
from autoencoder import SequenceAutoencoder
from config.settings import settings

# Load your dataset
def load_data():
    # Implement data loading logic
    pass

def train_model():
    data = load_data()
    dataset = TensorDataset(torch.Tensor(data))
    dataloader = DataLoader(dataset, batch_size=settings.BATCH_SIZE, shuffle=True)

    model = SequenceAutoencoder(input_size=1024, hidden_size=256)
    criterion = nn.MSELoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

    epochs = 10
    for epoch in range(epochs):
        for batch in dataloader:
            inputs = batch[0]
            outputs = model(inputs)
            loss = criterion(outputs, inputs)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        print(f"Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}")

    # Save the model
    torch.save(model.state_dict(), settings.MODEL_PATH)

if __name__ == "__main__":
    train_model()

---

Analysis Pipeline

src/pipeline/analysis.py

Implement the analysis pipeline:

# src/pipeline/analysis.py
import numpy as np
import torch
from src.ml.autoencoder import SequenceAutoencoder
from config.settings import settings
from Bio import SeqIO

class AnalysisPipeline:
    def __init__(self):
        self.model = SequenceAutoencoder(input_size=1024, hidden_size=256)
        self.model.load_state_dict(torch.load(settings.MODEL_PATH))
        self.model.eval()

    def process_sequence(self, raw_data):
        # Convert raw signal to sequence
        sequence = self._basecall(raw_data)
        
        # Detect modifications
        modifications = self._detect_modifications(sequence)
        
        # Analyze CRISPR edits
        edit_analysis = self._analyze_edits(sequence)
        
        return {
            'sequence': sequence,
            'modifications': modifications,
            'edit_analysis': edit_analysis
        }

    def _basecall(self, raw_data):
        # Implement basecalling logic using Oxford Nanopore's Guppy or similar tool
        pass

    def _detect_modifications(self, sequence):
        # Preprocess sequence data
        input_data = self._preprocess_sequence(sequence)
        input_tensor = torch.Tensor(input_data)
        
        # Pass through autoencoder
        with torch.no_grad():
            reconstructed = self.model(input_tensor)
        
        # Calculate reconstruction error
        error = torch.mean((input_tensor - reconstructed) ** 2, dim=1).numpy()
        modifications = error > settings.MODIFICATION_THRESHOLD
        return modifications.tolist()

    def _analyze_edits(self, sequence):
        # Implement CRISPR edit analysis
        pass

    def _preprocess_sequence(self, sequence):
        # Convert sequence to numerical representation
        nucleotide_map = {'A': 0, 'C': 1, 'G': 2, 'T': 3}
        numeric_sequence = [nucleotide_map.get(nuc, 4) for nuc in sequence]
        return numeric_sequence

Notes

• Basecalling: Use an external tool like Guppy for basecalling. Integrate it within _basecall.
• Preprocessing: Ensure the sequence is appropriately preprocessed before feeding it into the model.
• Edit Analysis: Implement logic to detect specific genetic edits relevant to your application.

---

API Development with FastAPI

src/main.py

Develop the FastAPI application:

# src/main.py
from fastapi import FastAPI, Depends, HTTPException
from sqlalchemy.orm import Session
from src.core.database import get_db
from src.core.minion_controller import MinIONController
from src.pipeline.analysis import AnalysisPipeline
from config.settings import settings
from src.models.schemas import SampleResult

app = FastAPI()
minion = MinIONController()
pipeline = AnalysisPipeline()

@app.post("/analyze_sample")
async def analyze_sample(sample_id: str, db: Session = Depends(get_db)):
    try:
        # Start sequencing
        run_id = await minion.start_sequencing(sample_id)
        
        # Wait for sequencing to complete or implement real-time data handling
        # For this example, we'll assume data is ready
        raw_data = await minion.get_sequence_data()
        
        # Process and analyze
        results = pipeline.process_sequence(raw_data)
        
        # Store results in the database
        sample_result = SampleResult(
            sample_id=sample_id,
            sequence=results['sequence'],
            modifications=results['modifications'],
            edit_analysis=results['edit_analysis']
        )
        db.add(sample_result)
        db.commit()
        db.refresh(sample_result)
        
        return {
            "run_id": run_id,
            "results": results
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

Running the API

Use Uvicorn to run your FastAPI application:

uvicorn src.main:app --reload

---

Testing

tests/test_pipeline.py

Implement unit tests for your analysis pipeline:

# tests/test_pipeline.py
import unittest
from src.pipeline.analysis import AnalysisPipeline

class TestAnalysisPipeline(unittest.TestCase):
    def setUp(self):
        self.pipeline = AnalysisPipeline()

    def test_process_sequence(self):
        # Provide sample raw data
        raw_data = "sample_raw_data"
        results = self.pipeline.process_sequence(raw_data)
        self.assertIn('sequence', results)
        self.assertIn('modifications', results)
        self.assertIn('edit_analysis', results)

if __name__ == '__main__':
    unittest.main()

Run your tests:

python -m unittest discover -s tests

---

Deployment

Dockerization (Optional)

Create a Dockerfile for containerization:

# Use an official Python runtime as a parent image
FROM python:3.9-slim

# Set the working directory
WORKDIR /app

# Copy the current directory contents into the container
COPY . /app

# Install any needed packages specified in requirements.txt
RUN pip install poetry
RUN poetry install

# Expose port
EXPOSE 8000

# Run the application
CMD ["poetry", "run", "uvicorn", "src.main:app", "--host", "0.0.0.0", "--port", "8000"]

Build and run your Docker image:

docker build -t genetic-testing .
docker run -p 8000:8000 genetic-testing

Production Server

For production deployment, consider using Gunicorn with Uvicorn workers:

poetry run gunicorn src.main:app -w 4 -k uvicorn.workers.UvicornWorker

---

References

1. Pydantic Settings Management: Pydantic Documentation
2. SQLAlchemy ORM: SQLAlchemy Documentation
3. MinKNOW API: MinKNOW API GitHub
4. FastAPI: FastAPI Documentation
5. PyTorch: PyTorch Documentation
6. Biopython: Biopython Tutorial

---

Conclusion

By following this guide, you will have a fully functional genetic testing platform capable of real-time sequencing data processing and analysis. The system is modular, allowing for easy expansion and customization based on specific requirements.

---

Note: Ensure all third-party tools like MinKNOW and Guppy are properly installed and configured on your system. Additionally, always handle sensitive data securely and comply with relevant regulations when dealing with genetic information.